To demonstrate the effectiveness of the proposed
deep attention model in predicting eye fixations, we evaluated it
by comparison to 13 state-of-the-art models, including six classical models:
ITTI [1]}, GBVS [2]}, Judd Model [3]},
BMS [4]}, CAS [5]}, AIM [6]},
and seven deep learning based models:
DeeFix [7]}, SALICON [8]}, Mr-CNN [9]}, SalNet [10]},
Deep Gaze I [11]}, eDN [12]}, and SU [13]}.
These methods have been proposed in recent years or widely used for comparison.
For the methods: ITTI [1]}, CAS [5]}, and AIM [6]}
that we calculated saliency maps using their publicly available code with the recommended parameters by the authors.
A summary of these models is provided in Table REF . As seen, most of the models
require off-line training or based on deep learning framework. Our model DVA (Deep Visual Attention),
is also included in Table REF . We report the computation time of above models with TORONTO [17]} dataset.
Since most deep learning based models have not been publicly available,
we only report the speed performance of Mr-CNN [9]}, SalNet [10]},
and eDN [12]} with other non-deep learning model.
As visible, the suggested model, DVA, achieves a fast speed (0.1s per frame).
